Call:
kknn(formula = 流失 ~ ., train = traindata, test = traindata, k = 5)
Response: "nominal"
fit prob.0 prob.1
1 1 0.33609798 0.66390202
2 1 0.25597771 0.74402229
3 0 0.97569952 0.02430048
4 0 0.51243637 0.48756363
5 0 1.00000000 0.00000000
6 1 0.17633839 0.82366161
7 0 0.59198438 0.40801562
8 0 1.00000000 0.00000000
9 0 1.00000000 0.00000000
10 1 0.36039846 0.63960154
11 0 0.74402229 0.25597771
12 0 0.84796209 0.15203791
13 0 0.89557925 0.10442075
14 0 1.00000000 0.00000000
15 0 1.00000000 0.00000000
16 0 0.74402229 0.25597771
17 1 0.02430048 0.97569952
18 1 0.15203791 0.84796209
19 0 1.00000000 0.00000000
20 0 0.97569952 0.02430048
21 0 0.97569952 0.02430048
22 0 1.00000000 0.00000000
23 0 0.76784183 0.23215817
24 0 0.74354135 0.25645865
25 0 0.74402229 0.25597771
26 0 1.00000000 0.00000000
27 0 1.00000000 0.00000000
28 0 0.51186411 0.48813589
29 0 1.00000000 0.00000000
30 0 0.97569952 0.02430048
31 0 1.00000000 0.00000000
32 0 0.56768390 0.43231610
33 0 0.84796209 0.15203791
34 0 0.66390202 0.33609798
35 1 0.00000000 1.00000000
36 0 1.00000000 0.00000000
37 0 1.00000000 0.00000000
38 0 0.84796209 0.15203791
39 1 0.25597771 0.74402229
40 1 0.48813589 0.51186411
41 1 0.36039846 0.63960154
42 0 0.97569952 0.02430048
43 0 0.89557925 0.10442075
44 0 1.00000000 0.00000000
45 0 0.51243637 0.48756363
46 0 0.66390202 0.33609798
47 0 0.74402229 0.25597771
48 0 1.00000000 0.00000000
49 0 1.00000000 0.00000000
50 0 1.00000000 0.00000000
51 1 0.36039846 0.63960154
52 0 0.91987973 0.08012027
53 0 1.00000000 0.00000000
54 0 0.91987973 0.08012027
55 1 0.25597771 0.74402229
56 0 0.89557925 0.10442075
57 0 0.91987973 0.08012027
58 0 1.00000000 0.00000000
59 0 0.56768390 0.43231610
60 1 0.48813589 0.51186411
61 0 1.00000000 0.00000000
62 0 1.00000000 0.00000000
63 1 0.48756363 0.51243637
64 0 0.51243637 0.48756363
65 0 0.51243637 0.48756363
66 0 0.84796209 0.15203791
67 0 0.84796209 0.15203791
68 0 0.76784183 0.23215817
69 1 0.02430048 0.97569952
70 1 0.00000000 1.00000000
71 0 0.84796209 0.15203791
72 0 0.76784183 0.23215817
73 0 0.76784183 0.23215817
74 1 0.15203791 0.84796209
75 1 0.02430048 0.97569952
76 0 1.00000000 0.00000000
77 0 0.51243637 0.48756363
78 1 0.36039846 0.63960154
79 0 0.71972181 0.28027819
80 0 0.82366161 0.17633839
81 1 0.36039846 0.63960154
82 1 0.23215817 0.76784183
83 0 0.76784183 0.23215817
84 1 0.00000000 1.00000000
85 0 1.00000000 0.00000000
86 0 0.66390202 0.33609798
87 0 1.00000000 0.00000000
88 0 0.91987973 0.08012027
89 1 0.23215817 0.76784183
90 0 1.00000000 0.00000000
91 0 0.91987973 0.08012027
92 0 1.00000000 0.00000000
93 0 1.00000000 0.00000000
94 1 0.10442075 0.89557925
95 0 0.91987973 0.08012027
96 0 0.74354135 0.25645865
97 1 0.25645865 0.74354135
98 1 0.33609798 0.66390202
99 0 0.91987973 0.08012027
100 1 0.25645865 0.74354135
101 0 1.00000000 0.00000000
102 1 0.28027819 0.71972181
103 0 0.66390202 0.33609798
104 0 0.51186411 0.48813589
105 0 0.56768390 0.43231610
106 0 0.84796209 0.15203791
107 0 0.76784183 0.23215817
108 0 1.00000000 0.00000000
109 0 0.76784183 0.23215817
110 0 0.91987973 0.08012027
111 1 0.43231610 0.56768390
112 0 1.00000000 0.00000000
113 0 0.97569952 0.02430048
114 0 1.00000000 0.00000000
115 0 0.76784183 0.23215817
116 0 0.63960154 0.36039846
117 0 0.97569952 0.02430048
118 1 0.15203791 0.84796209
119 0 0.74402229 0.25597771
120 0 1.00000000 0.00000000
121 0 1.00000000 0.00000000
122 0 0.91987973 0.08012027
123 0 1.00000000 0.00000000
124 0 0.74354135 0.25645865
125 0 1.00000000 0.00000000
126 1 0.43231610 0.56768390
127 0 0.71972181 0.28027819
128 1 0.08012027 0.91987973
129 0 0.91987973 0.08012027
130 1 0.10442075 0.89557925
131 0 1.00000000 0.00000000
132 0 0.91987973 0.08012027
133 0 0.51243637 0.48756363
134 0 0.66390202 0.33609798
135 1 0.02430048 0.97569952
136 0 1.00000000 0.00000000
137 0 0.74354135 0.25645865
138 0 0.97569952 0.02430048
139 0 1.00000000 0.00000000
140 0 1.00000000 0.00000000
141 1 0.25597771 0.74402229
142 0 1.00000000 0.00000000
143 0 1.00000000 0.00000000
144 1 0.36039846 0.63960154
145 0 1.00000000 0.00000000
146 0 0.74402229 0.25597771
147 0 0.84796209 0.15203791
148 0 0.91987973 0.08012027
149 0 0.51243637 0.48756363
150 0 1.00000000 0.00000000
151 1 0.28027819 0.71972181
152 0 1.00000000 0.00000000
153 0 0.59198438 0.40801562
154 0 0.51243637 0.48756363
155 1 0.33609798 0.66390202
156 0 0.97569952 0.02430048
157 0 1.00000000 0.00000000
158 0 1.00000000 0.00000000
159 0 0.59198438 0.40801562
160 1 0.48756363 0.51243637
161 0 1.00000000 0.00000000
162 0 0.97569952 0.02430048
163 1 0.25645865 0.74354135
164 1 0.33609798 0.66390202
165 0 0.51186411 0.48813589
166 1 0.43231610 0.56768390
167 0 1.00000000 0.00000000
168 0 1.00000000 0.00000000
169 0 1.00000000 0.00000000
170 0 1.00000000 0.00000000
171 0 1.00000000 0.00000000
172 0 0.76784183 0.23215817
173 0 0.56768390 0.43231610
174 0 0.76784183 0.23215817
175 0 0.84796209 0.15203791
176 0 1.00000000 0.00000000
177 0 0.51243637 0.48756363
178 0 0.51243637 0.48756363
179 0 0.63960154 0.36039846
180 0 0.74402229 0.25597771
181 0 1.00000000 0.00000000
182 1 0.15203791 0.84796209
183 0 0.66390202 0.33609798
184 1 0.02430048 0.97569952
185 0 0.97569952 0.02430048
186 1 0.23215817 0.76784183
187 0 0.97569952 0.02430048
188 0 0.51186411 0.48813589
189 1 0.25597771 0.74402229
190 0 1.00000000 0.00000000
191 0 1.00000000 0.00000000
192 0 1.00000000 0.00000000
193 0 1.00000000 0.00000000
194 0 1.00000000 0.00000000
195 0 0.91987973 0.08012027
196 0 1.00000000 0.00000000
197 0 0.91987973 0.08012027
198 0 0.74402229 0.25597771
199 0 1.00000000 0.00000000
200 0 1.00000000 0.00000000
201 0 1.00000000 0.00000000
202 0 1.00000000 0.00000000
203 0 0.97569952 0.02430048
204 0 0.84796209 0.15203791
205 1 0.00000000 1.00000000
206 0 0.89557925 0.10442075
207 0 1.00000000 0.00000000
208 0 1.00000000 0.00000000
209 0 0.91987973 0.08012027
210 0 0.84796209 0.15203791
211 0 1.00000000 0.00000000
212 0 0.74402229 0.25597771
213 0 0.74402229 0.25597771
214 0 0.66390202 0.33609798
215 0 1.00000000 0.00000000
216 0 0.91987973 0.08012027
217 0 1.00000000 0.00000000
218 0 1.00000000 0.00000000
219 0 0.51186411 0.48813589
220 0 1.00000000 0.00000000
221 1 0.00000000 1.00000000
222 1 0.15203791 0.84796209
223 0 0.51243637 0.48756363
224 1 0.28027819 0.71972181
225 1 0.08012027 0.91987973
226 0 1.00000000 0.00000000
227 0 1.00000000 0.00000000
228 1 0.25597771 0.74402229
229 1 0.15203791 0.84796209
230 1 0.15203791 0.84796209
231 0 0.51243637 0.48756363
232 1 0.08012027 0.91987973
233 1 0.28027819 0.71972181
234 1 0.40801562 0.59198438
235 0 0.51186411 0.48813589
236 0 1.00000000 0.00000000
237 1 0.43231610 0.56768390
238 0 0.89557925 0.10442075
239 1 0.33609798 0.66390202
240 0 0.74354135 0.25645865
241 0 0.97569952 0.02430048
242 0 1.00000000 0.00000000
243 0 0.97569952 0.02430048
244 0 0.89557925 0.10442075
245 0 0.74402229 0.25597771
246 0 1.00000000 0.00000000
247 0 1.00000000 0.00000000
248 0 0.84796209 0.15203791
249 1 0.36039846 0.63960154
250 0 0.84796209 0.15203791
251 1 0.48813589 0.51186411
252 0 1.00000000 0.00000000
253 0 1.00000000 0.00000000
254 0 0.91987973 0.08012027
255 0 0.56768390 0.43231610
256 0 1.00000000 0.00000000
257 0 0.84796209 0.15203791
258 1 0.33609798 0.66390202
259 0 0.76784183 0.23215817
260 0 1.00000000 0.00000000
261 1 0.36039846 0.63960154
262 0 1.00000000 0.00000000
263 0 1.00000000 0.00000000
264 1 0.48756363 0.51243637
265 1 0.48756363 0.51243637
266 0 0.89557925 0.10442075
267 0 1.00000000 0.00000000
268 0 0.66390202 0.33609798
269 0 0.56768390 0.43231610
270 0 0.74402229 0.25597771
271 1 0.25597771 0.74402229
272 0 1.00000000 0.00000000
273 0 0.66390202 0.33609798
274 0 1.00000000 0.00000000
275 0 1.00000000 0.00000000
276 0 0.89557925 0.10442075
277 0 1.00000000 0.00000000
278 0 0.51243637 0.48756363
279 0 0.84796209 0.15203791
280 0 1.00000000 0.00000000
281 0 0.84796209 0.15203791
282 0 0.91987973 0.08012027
283 0 1.00000000 0.00000000
284 0 1.00000000 0.00000000
285 0 0.97569952 0.02430048
286 0 1.00000000 0.00000000
287 0 1.00000000 0.00000000
288 0 1.00000000 0.00000000
289 0 1.00000000 0.00000000
290 0 0.91987973 0.08012027
291 0 1.00000000 0.00000000
292 0 1.00000000 0.00000000
293 0 0.91987973 0.08012027
294 0 0.76784183 0.23215817
295 1 0.17633839 0.82366161
296 1 0.10442075 0.89557925
297 0 0.84796209 0.15203791
298 0 0.97569952 0.02430048
299 1 0.36039846 0.63960154
300 0 1.00000000 0.00000000
301 0 0.84796209 0.15203791
302 0 0.91987973 0.08012027
303 0 0.89557925 0.10442075
304 0 0.97569952 0.02430048
305 0 1.00000000 0.00000000
306 1 0.02430048 0.97569952
307 1 0.15203791 0.84796209
308 1 0.40801562 0.59198438
309 0 0.84796209 0.15203791
310 1 0.00000000 1.00000000
311 0 0.89557925 0.10442075
312 0 1.00000000 0.00000000
313 0 1.00000000 0.00000000
314 1 0.48756363 0.51243637
315 0 0.51243637 0.48756363
316 0 0.97569952 0.02430048
317 0 1.00000000 0.00000000
318 0 0.97569952 0.02430048
319 1 0.25645865 0.74354135
320 0 1.00000000 0.00000000
321 1 0.08012027 0.91987973
322 1 0.33609798 0.66390202
323 0 0.91987973 0.08012027
324 0 0.89557925 0.10442075
325 0 0.91987973 0.08012027
326 0 1.00000000 0.00000000
327 1 0.25597771 0.74402229
328 0 1.00000000 0.00000000
329 0 1.00000000 0.00000000
330 1 0.43231610 0.56768390
331 0 0.84796209 0.15203791
332 0 0.51243637 0.48756363
333 1 0.33609798 0.66390202
[ reached 'max' / getOption("max.print") -- omitted 365 rows ]